e2d | gossip-based etcd manager | Key Value Database library
kandi X-RAY | e2d Summary
kandi X-RAY | e2d Summary
e2d is designed to manage highly available etcd clusters in the cloud. It can be configured to interact directly with your cloud provider to seed the cluster membership and backup/restore etcd data.
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Top functions reviewed by kandi - BETA
- skipE2Dpb skips E2Dpb bytes .
- newRunCmd returns cobra . Command for running subcommands
- readStructFields returns a map of fields from t .
- newPKIGenCertsCmd returns a Command instance for PKIGen certificates
- New returns a new Manager .
- startEtcdCluster starts etcd cluster
- toString converts data to string .
- setValue sets s to value v .
- ParseSnapshotBackupURL parses a backup backup URL
- getExistingNameFromDataDir gets the name from the data dir if it exists
e2d Key Features
e2d Examples and Code Snippets
Community Discussions
Trending Discussions on Key Value Database
QUESTION
I'm developing a Laravel application & started using Redis as a caching system. I'm thinking of caching the data of all of a specific model I have, as a user may make an API request that this model is involved in quite often. Would a valid solution be storing each model in a hash, where the field is that record's unique ID, and the values are just the unique model's data, or is this use case too complicated for a simple key value database like Redis? I"m also curious as to how I would create model instances from the hash, when I retrieve all the data from it. Replies are appreciated!
...ANSWER
Answered 2021-Jul-08 at 17:02Short answer: Yes, you can store a model, or collections, or basically anything in the key-value caching of Redis. As long as the key provided is unique and can be retraced. Redis could even be used as a primary database.
Long answer
Ultimately, I think it depends on the implementation. There is a lot of optimization that can be done before someone can/should consider caching all models. For "simple" records that involve large datasets, I would advise to first optimize your queries and code and check the results. Examples:
- Select only data you need, not entire models.
- Use the Database Query Builder for interacting with the database when targeting large records, rather than Eloquent (Eloquent is significantly slower due to the Active Record pattern).
- Consider using the
toBase()method. This retrieves all data but does not create the Eloquent model, saving precious resources. - Use tools like the Laravel debugbar to analyze and discover potential long query loads.
For large datasets that do not change often or optimization is not possible anymore: caching is the way to go!
There is no right answer here, but maybe this helps you on your way! There are plenty of packages that implement similar behaviour.
QUESTION
In many articles, I've read that compacted Kafka topics can be used as a database. However, when looking at the Kafka API, I cannot find methods that allow me to query a topic for a value based on a key.
So, can a compacted Kafka topic be used as a (high performance, read-only) key-value database?
In my architecture I want to feed a component with a compacted topic. And I'm wondering whether that component needs to have a replica of that topic in its local database, or whether it can use that compacted topic as a key value database instead.
...ANSWER
Answered 2020-Nov-25 at 01:12Compacted kafka topics themselves and basic Consumer/Producer kafka APIs are not suitable for a key-value database. They are, however, widely used as a backstore to persist KV Database/Cache data, i.e: in a write-through approach for instance. If you need to re-warmup your Cache for some reason, just replay the entire topic to repopulate.
In the Kafka world you have the Kafka Streams API which allows you to expose the state of your application, i.e: for your KV use case it could be the latest state of an order, by the means of queriable state stores. A state store is an abstraction of a KV Database and are actually implemented using a fast KV database called RocksDB which, in case of disaster, are fully recoverable because it's full data is persisted in a kafka topic, so it's quite resilient as to be a source of the data for your use case.
Imagine that this is your Kafka Streams Application architecture:
To be able to query these Kafka Streams state stores you need to bundle an HTTP Server and REST API in your Kafka Streams applications to query its local or remote state store (Kafka distributes/shards data across multiple partitions in a topic to enable parallel processing and high availability, and so does Kafka Streams). Because Kafka Streams API provides the metadata for you to know in which instance the key resides, you can surely query any instance and, if the key exists, a response can be returned regardless of the instance where the key lives.
With this approach, you can kill two birds in a shot:
- Do stateful stream processing at scale with Kafka Streams
- Expose its state to external clients in a KV Database query pattern style
All in a real-time, highly performant, distributed and resilient architecture.
The images were sourced from a wider article by Robert Schmid where you can find additional details and a prototype to implement queriable state stores with Kafka Streams.
Notable mention:
If you are not in the mood to implement all of this using the Kafka Streams API, take a look at ksqlDB from Confluent which provides an even higher level abstraction on top of Kafka Streams just using a cool and simple SQL dialect to achieve the same sort of use case using pull queries. If you want to prototype something really quickly, take a look at this answer by Robin Moffatt or even this blog post to get a grip on its simplicity.
While ksqlDB is not part of the Apache Kafka project, it's open-source, free and is built on top of the Kafka Streams API.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install e2d
Periodic backups can be made of the entire database, and e2d automates both creating these snapshot backups, as well as, restoring them in the event of a disaster. Getting started with periodic snapshots only requires passing a file location to --snapshot-backup-url. The url is then parsed to determine the target storage and location. When e2d first starts up, the presence of a valid backup file at the provided URL indicates it should attempt to restore from this snapshot. The internal database layout of etcd lends itself to being compressed. This is why e2d allows for snapshots to be compressed in-memory at the time of creation. To enable gzip compression, use the --snapshot-compression flag. Snapshot storage options like S3 use TLS and offer encryption-at-rest, however, it is possible that encryption of the snapshot file itself might be needed. This is especially true for other storage options that do not offer these features. Enabling snapshot encryption is simply --snapshot-encryption. The encryption key itself is derived only from the CA private key, so enabling encryption also requires passing --ca-key <key path>. The encryption being used is AES-256 in CTR mode, with message authentication provided by HMAC-512_256. This mode was used because the Go implementation of AES-GCM would require the entire snapshot to be in-memory, and CTR mode allows for memory efficient streaming. It is possible to use compression alongside of encryption, however, it is important to note that because of the possibility of opening up side-channel attacks, compression is not performed before encryption. The nature of how strong encryption works causes the encrypted snapshot to not gain benefits from compression. So enabling snapshot compression with encryption will cause the gzip level to be set to gzip.NoCompression, meaning it still creates a valid gzip file, but doesn't waste nearly as many compute resources while doing so.
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